Retrieve detailed information about all OSU buildings, including addresses, locations, departments, and room details.
MIT
736,940 tools. Updated 2026-11-02 16:44
"osu!" matching MCP tools:
- Retrieve addresses, hours, and contact details for all OSU library locations.MIT
- Resolve ambiguous labels, paths, or partial IDs to canonical UIDs for coding graph lookups. Use when exact identifiers are unknown.Apache 2.0
- Resolve known symbols, paths, or UIDs via lexical lookup and graph expansion, returning filtered nodes by kind, depth, and confidence. Attach ancestor chains for breadcrumbs with include_spine.Apache 2.0
- Analyze dependency graphs to group affected nodes into risk tiers: will_break, should_review, context. Choose downstream or upstream traversal to see what breaks or what a change depends on.Apache 2.0
- Search stored observations and learned patterns with signal-ranked results. Filter by confidence and age to retrieve relevant past experience for current tasks.Apache 2.0
Matching MCP Servers
- AlicenseNot gradedqualityCmaintenanceMCP server that exposes the osu! API v2 as tools, allowing natural language queries about osu! profiles, beatmaps, rankings, and more.29 PyPIMIT
- AlicenseCqualityDmaintenanceProvides over 50 tools across 14 categories for accessing Ohio State University services including transportation, academics, dining, and events.494MIT
Matching MCP Connectors
- Samuelz Business OSOAuth
Know where your business stands and what needs attention next. Review available inquiry and paid-work records, goals and recorded spending from your connected workspace. See what needs attention and which conclusions the data cannot yet support. Create non-money goals on explicit request. Revenue targets and increased spending limits require owner approval. Requires a Samuelz account with the relevant Business OS modules. Data coverage depends on connected sources; missing data stays unknown. OAuth access remains bound to one workspace and its current permissions. By Glöggl GmbH.
- Port OS Smart HomeOAuth
Control your existing Philips lights, Sonos speakers, Roborock vacuums and other supported devices through Port OS cloud. Give ChatGPT, Claude and other MCP-compatible assistants access to the devices you connect. Turn off the living room lights, play music on your speakers, adjust the thermostat, run a vacuum in a room, check a camera snapshot or find a connected tracker. Available actions depend on your linked provider accounts and each device’s supported capabilities. Port OS runs in the cloud; no local server is required. You choose the account, device and capability permissions each assistant receives. Device operations require user OAuth; public tool discovery does not grant device access. Port OS is in alpha. Setup: https://portoperatingsystem.com/docs/getting-started/ Support: https://portoperatingsystem.com/contact/
- List all recorded project workspaces, showing each one's phase, last update time, and underlying memory ID, to monitor ongoing work.MIT
- Upload files, text, or base64 content to Aliyun OSS. Provide a local file path or inline content with filename to store under an agents/ prefix, then get status, size, key, and URL.MIT
- Retrieve relevant documentation chunks through semantic and lexical search, using metadata pre-filters for domain, layer, and recency to ensure results reflect current, correct context.Apache 2.0
- Find dead-code candidates by surfacing functions, methods, and classes with no non-test references. Filter by kind or include tests to isolate unused symbols.Apache 2.0
- Get callers, callees, siblings, and referenced docs around any code symbol to map its relationships and dependencies.Apache 2.0
- Search code symbols using natural-language descriptions. Combines semantic, lexical, and centrality matching to locate relevant functions and classes when exact names are unknown.Apache 2.0
- Discover what references a code, doc, or folder by listing inbound graph edges. Specify a node UID and optional edge kinds to see dependents, callers, or importers.Apache 2.0
- Record whether a suggested pattern was helpful, then adjust its confidence using brain-inspired formulas for more accurate future validation.Apache 2.0
- Signal which retrieval results the agent actually used, so priority learning weights them higher. Cite retrievals after reading or applying chunks to improve future relevance.Apache 2.0
- Adjust document chunk priority from past retrieval outcomes: boost chunks cited in successful tasks, lower those in rework, and refine retrieval quality automatically.Apache 2.0
- Evaluates retrieval quality over a lookback period and recommends whether to enable contextual enrichment, delivering an auditable, cost-aware decision upfront.Apache 2.0
- Apply changes to the user's to-do backlog in one save: edit, complete, move, archive, or delete tasks and attach files, with per-operation results.MITDestructive
- Detect indexed files with Git commits newer than the index pass that read them, revealing stale graph entries before you trust a complete answer.Apache 2.0
- Suggest task-relevant patterns matched to domain, complexity, and task type, using spaced repetition to prioritize fading validated patterns for re-validation.Apache 2.0
- Determine if contextual enrichment is worth LLM cost by reporting mean retrieval precision over a lookback window, based on citation and outcome pairs.Apache 2.0